AI address validation: Can you really verify an address with AI?
AI address validation (also referred to as AI address verification) is the sometimes unpredictable and inaccurate process of checking that an address is valid or verified using Large Language Models (LLMs) like ChatGPT, Claude, Gemini, Deepseek and more.
We say unpredictable and inaccurate for several reasons, which we’ll dive into below, but the main reasons that verifying an address with an LLM (or standardizing an address with GenAI for that matter) boil down to this: generative AI tools don’t have access to authoritative address data, they ingest “everything” without context, and that creates false positives—harming your data quality, address match accuracy, and customer experience.
Smarty recommends applying a personal (and professional) touch to address data validation to avoid AI-induced headaches. Our APIs are purpose-built to handle the nuance of address components, data integration, error handling, and data cleansing—so your customer data stays accurate and your customer service team stays happy.
You can try our APIs for free below or keep reading to learn why using ChatGPT (or any GenAI) for verifying addresses could tank your data accuracy and customer satisfaction.
To answer the question, “Can you verify an address with AI?”, we need to dig deeper into these topics:
- The AI hype
- What is AI address validation?
- Can you verify an address with an LLM?
- Using ChatGPT, Claude, Gemini, and other GenAI tools for address parsing and standardization
- Why LLMs can’t replace dedicated address validation APIs
- What LLMs are good at, alongside address verification APIs
- Conclusion
- AI address verification FAQs
The AI hype
In early 2022, OpenAI launched ChatGPT, a new AI model that allowed a supercomputer to do the searching for its users from the Azure AI supercomputing infrastructure. After reaching over 100 million monthly active users in only 2 months, other competitors wanted a piece of the pie.
Since then, people around the world have decided they’d rather not perform an endless Google scroll to find what they need. They’re now in favor of letting other artificial intelligence models do the site scraping for them. As usage increased, users began noticing quirky, nonsensical, and even inaccurate AI data generation.
These inaccuracies were coined “AI hallucinations,” the phenomenon where these tools perceive patterns or objects that are nonexistent and put them forth as facts, which hurts your data quality and increases your error rate.
Popular AI tools
Many AI search tools exist. We’ve listed several below, alongside a brief description of what the language model was originally created for. Many AI tools are being used in isolation for identity or address verification, but it’s our expert opinion that they shouldn’t be.
- ChatGPT: Many credit this machine learning model as being the first to be widely accepted and used. According to their site, ChatGPT consists of an artificial intelligence-based service to organize and summarize information, assist with translations, analyze or generate images, respond to questions, analyze data relationships, and make predictions when generating a response.
- AI Mode (Gemini): AI Overviews on Google also have a place in our AI toolbox. These overviews are usually at the top of a Google search, but turning on AI Mode allows you to ask the overview questions. AI Mode helps with gathering more information on a topic through text and links while bringing in relevant infographics and connected sites to answer the more complex questions that humans are asking.
- Grok: Developed by Elon Musk’s company xAI, Grok can access real-time information through the web. Like other AI chatbots, Grok is programmed to respond to questions and develop content, but it’s mainly programmed to “maximize truth and objectivity… answering spicy questions with witty and rebellious answers.”
- Claude: Developed by the research firm Anthropic, Claude is a multimodal large language model that can search text, images, audio, and visual inputs to summarize, answer questions, program code, generate documents, create diagrams, and much more.
Nowhere do any of them say they were built for address verification. Because frankly, none of them were.
What is AI address validation?
AI address validation is an interesting, yet misguided attempt by a person or company to verify that address data is valid using AI chatbots like ChatGPT, Claude, etc. It usually involves uploading an address or a list of addresses into GenAI platforms and then asking the platform to verify it.
Can you verify an address with an LLM?
You certainly can, and it works about as well as using a magic 8 ball would. Each time you submit that address, you will likely get a different answer.
While many AI Chatbots are somewhat reliable and will straight up tell you that they can’t directly validate an address, others will simply provide the answer they think you want to hear, that the addresses you provided are real and valid, and you can start sending your products, mailers, and anything else you like to that destination.
The problem comes down to this: we only trained LLMs on information that we DO know. But most of the time, people don’t write research papers, blogs, articles, etc., on what they don’t know. People don’t write essays talking about what something is not.
For example, we don’t write essays about what an apple isn’t. Therefore, there is no information out there on what is NOT an apple. We’ve created a somewhat arrogant artificial intelligence that isn’t trained on how to say when it doesn’t know something. Rather than admitting it doesn’t know, GenAI will “lie” or make something up in order to maintain the appearance it has been trained to keep up, that it is all knowing on any and all topics, even ones it has never been trained on.
We certainly can’t force you NOT to still attempt to verify an address or list of addresses with GenAI, but this could lead to poor address match rate accuracy, data quality issues, and unhappy customers; will this deeply persuasive article prevent you from doing so?
Pretty, pretty please?
Should you verify an address with GenAI?
The short and long answer to this question is no, you should not use ChatGPT, Grok, or any other generative AI tools to validate your addresses. While LLMs like ChatGPT and Claude can sometimes help parse address-like strings into address components (e.g., recognizing “123 Main St, Provo, UT” and splitting it into street, city, and state), they don’t understand address data authority or how bad data impacts customer service and customer satisfaction.
Using ChatGPT, Claude, Gemini, and other GenAI tools for address parsing and standardization
These tools don’t know whether “123 Main St” actually exists, is deliverable, or has valid metadata like ZIP+4 or rooftop geocode. At best, they can reformat addresses and guess at missing pieces, but guesses aren’t good enough when data accuracy and deliverability matter, and those aren’t the only limitations that address verification through AI poses for your business.
An area where you might consider using GenAI tools could be address parsing or standardization, provided that you give the AI the rules it should follow regarding structure first. The problem will still remain: Address verification isn’t possible with artificial intelligence tools, so your data quality and data accuracy are at risk. We strongly recommend you have other checks in place to make sure the AI output is accurate before you make any location-based marketing decisions.
Limitations of AI address verification
Let’s summarize the main drawbacks here:
- Speed: LLMs take seconds per query, whereas purpose-built Smarty APIs validate 25,000+ per second for standard plans. And Smarty has actually hit millions per second for special use cases.
- Inconsistency: Ask the same question twice and you might get two different answers. Not great for business-critical workflows.
- Confidence: LLMs often fabricate data (“hallucinate”) when they can’t find an answer, presenting guesses as fact, creating data quality problems, and inflating your error rate and match rates.
- Security and privacy: You’re sending sensitive customer data to third-party servers who are not under contract to protect that information and may store or learn from it—not ideal for customer service or trust.
Why LLMs can’t replace dedicated address validation APIs
At first glance, it’s easy to see why someone might wonder if an AI like ChatGPT or Claude could handle address validation. After all, these tools can write essays, summarize data, and even generate poetry about your dog’s birthday.
But when it comes to validating addresses, they simply fall short of the capabilities of an address validation API.
Address validation APIs are SO much faster
These APIs are also incredibly fast and consistent. Where an AI chatbot might take several seconds to return an answer—and potentially a different answer every time you ask— a purpose-built API can process thousands and even millions of addresses per second with unwavering data accuracy. You can trust that the answer you get is the same answer you’d get if you asked a thousand times.
LLMs don’t have SmartyKey®
Because GenAI and LLMs don’t have access to proprietary data and authoritative data sets, they also don’t have access to persistent, unique identifiers (PUIDs). Tiny but mighty, an address PUID is the number that’s attached to an address and tracks all of the address's changes over time. While address validating software like Smarty’s tools has a PUID attached to every address, artificial intelligence doesn’t. It may be able to tell you that an address looks valid and correct, but it can’t tell you if that address is a duplicate of another address in your database or has gone through changes over time. Say goodbye to lots of marketing dollars being spent or sound address analysis being done on your part with GenAI as your address sidekick.
Address APIs are more secure than GenAI
Security and privacy are other big reasons that LLMs don’t cut it here. We previously touched on this, but it’s worth noting again for you skimmers out there.
When you feed sensitive customer addresses into an AI chatbot, you’re sending that data to someone else’s servers, which aren’t under a contract to protect that information. That customer data may be stored, reused, or even inadvertently exposed. By contrast, dedicated APIs are designed with strict data privacy controls and, in many cases, even allow you to keep data on your own servers.
Not only that, but LLMs start up and go out of business daily. They get their training data from anywhere that they can, and, as new startups, they don’t typically have a plan on what to do when they go bankrupt or what happens to the data they’ve ingested.
Who’s in charge here? Not GenAI
Perhaps most importantly, LLMs simply aren’t authoritative. They’re trained on a mix of public text, which means their “knowledge” is only as good as the articles, forums, and blogs they’ve ingested. They don’t actually know whether any address exists, but they’ll confidently tell you it does, even when it doesn’t.
So while AI has its place as a creative assistant, a pattern spotter, a research helper, and as an organizational tool, it’s not the right tool for the job when data quality, error handling, speed, consistency, and security really matter.
For address verification, you don’t want a guess.
You want a guarantee.
And that’s where dedicated APIs shine—offering address data enrichment, rooftop-level geocoding accuracy, and excellent customer experience outcomes.
What LLMs are good at, alongside address verification APIs
It’s always been interesting that a new thing comes out, and human nature is to immediately find reasons not to embrace it. We at Smarty do recognize the benefit that can be gained from using large language models and AI chats for a lot of things, even things that touch addresses.
Finding patterns
For instance, GenAI is really great at finding patterns in data. You could upload a file around 10,000 addresses at a time (assuming you don’t need to keep the data private) and ask for it to analyze the data and point out patterns, or better yet, ask it to find what patterns may or may not exist with a specific prompt like, “How many addresses include the word ‘street’ in this dataset?” or “How many addresses have commonly misspelled street addresses from this list?”
Most of the time, using an LLM to find and analyze patterns found in already-API-standardized lists is an effective way to utilize AI’s power for good.
Quick count analysis
Gen AI is also really great at quick count analysis. You could ask it, “How many addresses from this list are located in Grand Junction?” or “How many addresses include predirectionals in their street address?” and the LLM could typically give you the exact count, saving tons of person-hours from having to do this manually.
Summarizing and synthesizing information
If you feed a bunch of address data and property enrichment points into an AI machine, you’ll likely be able to see an excellent summary of that information, as well as some analysis on how that information can be useful for your business.
Generating test data
After using address data APIs to validate and standardize your list, you can send that list to GenAI and ask it to pull all military-type addresses, ones with unique ZIP Codes, etc., to build a sample set of data for further analysis and deeper understanding.
Organizing your information
Additionally, AI and LLMs are great at organizing the information that is already cleaned and deduplicated. You can have these tools organize the data by state, by country, by alphabetical or numerical order, etc. Artificial intelligence was made to streamline this process for you, but again, we recommend you organize clean, standardized data, rather than messy or aggregate datasets.
Conclusion
AI tools like ChatGPT, Claude, and Gemini are impressive, versatile, and even entertaining. But when it comes to verifying addresses, GenAI just doesn’t cut it.
Address validation isn’t about making a best guess or sounding confident.
It’s about maintaining clean address data, ensuring customer satisfaction, and supporting great customer service, because you know that an address exists, is deliverable, and meets authoritative standards.
Large Language Models are trained on publicly available information, not on the curated, up-to-date, and proprietary datasets that true address verification requires. They are slow, inconsistent, and prone to fabricating answers, which can lead to undeliverable mail, wasted money, and unhappy customers.
If you want data accuracy, speed, consistency, and privacy you can trust, dedicated address validation APIs are the clear choice. Experience data integration, data cleansing, data enrichment, and low error rates that are built in. Why settle for a hallucination when you can have a guarantee?
Try Smarty’s APIs for free today and see how real address verification is done, no guesswork required.
AI Address Verification FAQs
Can ChatGPT validate addresses?
No. It can reformat and guess, but it cannot confirm that an address is real or deliverable.
What’s the difference between address parsing & validation?
Parsing = breaking an address into parts (like street & city).
Validation = checking that it exists & is deliverable.
LLMs can parse okay, but they can’t validate because they don’t have access to a database with authoritative and proprietary information to check against.
Are LLMs good enough for mailing addresses?
Not if you want reliability. They’re helpful for research & formatting, but PLEASE don’t use GenAI for address validation.